Clinical trials demonstrate that high risk individuals can reduce their risk of diabetes by more than half when they follow a well-structured, intensive, life style modification program(6
). Therefore, early diagnosis could be crucial to reduce the global burden of diabetes. Widespread blood glucose testing may not be the best way to identify undiagnosed diabetes in large community or resource limited settings. Indeed, existing recommendations for diabetes screening that rely on blood testing are not widely followed, resulting in 30% of diabetics going undiagnosed(4
Our goal was to develop a screening score that can be used in a wide variety of community settings and clinical encounters (including patient waiting rooms or internet) via a simple pencil-and-paper method. Our new diabetes score appeared to perform better than existing methods by quantitative criteria. We believe that it also has good feasibility characteristics – as a simple (with 6 easily answered health-related questions) and efficient (with minimal time needed for survey and no need for a calculator with the maximum score less than 10) screening score with which patients and health care providers can assess their or their patients’ need for formal diabetes testing.
We found that the national guidelines for diabetes screening did not perform very well. The three diabetes risk assessment scores showed lower overall accuracy and tended to select larger proportions of people for diabetes screening compared to our new score. Low specificities of existing methods have been reported previously(33
). The screening criteria recommended by different organizations were developed using different frameworks and purposes, e.g., to enhance efficiency of screening or to target those who could benefit most from screening. So although they differ in numerical performance characteristics (e.g., sensitivity and specificity) based on our analysis, they may be more appropriate for those purposes.
The primary endpoint in our study was undiagnosed diabetes rather than the composite outcome of undiagnosed diabetes and pre-diabetes, but the same questionnaire may well be justified for these closely related outcomes (a disease and its precursor) with different cutpoints (5 for diabetes and 4 for pre-diabetes) based on the evidence obtained from our ancillary analyses. In addition, our score is for prediction of currently undiagnosed diabetes and not for incident diabetes in the future. However, strong consistency in risk factors for the prediction of prevalent and incident events in diabetes and other chronic diseases has been reported(36
), and we expect that the same set of risk factors in our model play important roles in the prediction of future diabetes or pre-diabetes. Nonetheless, other laboratory or behavioral/lifestyle variables could be useful in predicting future events rather than current events(18
A risk prediction approach that can capture a continuous risk spectrum is a popular tool that has been used to identify important risk factors and to estimate average risk; results can be used in decision making about public health and clinical care. Risk prediction has even been proposed as an alternative to diagnosis for some diseases(42
). We believe that ideal risk assessment methods or prediction models should be derived from large representative samples of a target population and consist of fixed and modifiable risk factors together. Simplicity and user-friendliness (including optimal presentation), in addition to accuracy, are keys for successful implementation and utilization, especially for lay persons(25
). To achieve these goals, we 1) adopted a statistical method that yields a systematic scoring system and accounts for design effects of the study appropriately (i.e., logistic regression model suited for complex survey data); 2) carefully selected a parsimonious set of predictors (guided not only by numerical and scientific evidence but also by feasibility perspectives); 3) chose categorized variables in intuitive or well-accepted ways (e.g., using deciles for age and obesity definition); and 4) emphasized an educational purpose of the screening score, highlighting the important risk factors to motivate high-risk people to be screened or to modify health behaviors (e.g., combining body mass index and waist circumference together, rather than using height, weight and waist separately). This combination of factors may explain the enhanced properties of our new score.
For this study, we tried to identify all existing screening guidelines or risk assessment scores for prevalent undiagnosed diabetes available for the U.S. population and one best-suited score for non-U.S. population for comparisons. We found that there are 3 national guidelines and 2 scores/questionnaires for diabetes screening in the U.S., whereas many prediction models exist for incident diabetes. Our search for the best suited non-U.S. model was guided by recent comparison studies(36
); we selected the Rotterdam model as it was developed for prevalent undiagnosed diabetes, has been externally validated in different samples, and only requires routinely available demographic or health information in its simple scoring system.
This study does have some limitations. First, some variables that are parts of existing methods (e.g., gestational diabetes) were not available in the databases we used. Therefore, some caution should be exercised in making comparisons between our and others’ methods. Nonetheless, we believe that the vast majority of key information was available and utilized, minimizing the unfairness in the comparisons. Second, we could not incorporate oral glucose tolerance test results because these data were not collected in the newer NHANES (1999–2006) and in the baseline visits in ARIC and CHS. Thus, we defined the outcome based solely on the FPG. The FPG is a recommended screening test, however, and the lack of oral glucose tolerance test data has not been shown previously to affect the stability of diabetes risk assessment methods(4
). Our results seemed to be robust to different definitions of the endpoint, either based on FPG or Hemoglobin A1c (e.g., AUC=0.79 vs. 0.78).
Although the lay population is increasingly appreciating the danger of diabetes and its complications, more education is still needed in community and clinical settings. In that sense, although further validation of our screening score in other samples is important, this newly developed algorithm could still have immediate applications. In addition to its use in clinical encounters, targeted screenings, and health education programs, the screening score can be applied by health plans to existing databases for case-finding. The new algorithm can also potentially help identify optimal populations for enrollment in clinical trials that test new strategies to prevent or manage diabetes.
In conclusion, we envision our screening score to serve as a method for identifying individuals in need of formal diabetes screening and calling for more attention to pre-diabetes. A self-assessment method that helps people decide whether they should seek medical care for diabetes testing may serve as one way to address the lack of interaction with health care facilities/providers that may underlie the high percentage of the population with undiagnosed diabetes, particularly the underserved. Although a consensus on diabetes screening has not yet been reached(45
), we believe a priority in formal screening for undiagnosed diabetes should be given to those who are at high risk. This new diabetes screening score could help identify these high risk individuals, while patients and caregivers alike await more definitive evidence-based recommendations(47